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多Agent 主从粒子群分布式计算框架
引用本文:郑宇军,陈胜勇,凌海风,徐新黎.多Agent 主从粒子群分布式计算框架[J].软件学报,2012,23(11):3000-3008.
作者姓名:郑宇军  陈胜勇  凌海风  徐新黎
作者单位:1. 浙江工业大学 计算机科学与技术学院,浙江 杭州 310023
2. 解放军理工大学 机械工程系,江苏 南京 210007
基金项目:国家自然科学基金(61105073,61173096,61103140,61020106009,61070043);浙江省自然科学基金(R1110679)
摘    要:面向大规模复杂优化问题,提出了一个基于并行粒子群优化的分布式Agent计算框架.框架中使用一个主群(master swarm)来演化问题的完整解,并使用一组从群(slave swarm)来并行优化一组子问题的解,主群和从群通过交替执行来提高问题的求解效率.采用异步组结构,主群/从群中的各类Agent共享一个解群,并通过相互协作,对解群进行构造、改进、修补、分解和合并等演化操作.该框架可用于求解复杂的约束多目标优化问题.通过一类典型运输问题上的实验,其结果表明,所提出的方法明显优于另外两种先进的演化算法.

关 键 词:agent  粒子群优化  主从模型  协同进化  分布式计算
收稿时间:6/9/2012 12:00:00 AM
修稿时间:2012/8/21 0:00:00

Multi-Agent Based Distributed Computing Framework for Master-Slave Particle Swarms
ZHENG Yu-Jun,CHEN Sheng-Yong,LING Hai-Feng and XU Xin-Li.Multi-Agent Based Distributed Computing Framework for Master-Slave Particle Swarms[J].Journal of Software,2012,23(11):3000-3008.
Authors:ZHENG Yu-Jun  CHEN Sheng-Yong  LING Hai-Feng and XU Xin-Li
Affiliation:1(College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China) 2(Department of Mechanical Engineering,PLA University of Science and Technology,Nanjing 210007,China)
Abstract:To effectively solve large-scale optimization problems, the paper proposes a distributed agent computing framework based on the parallel particle swarm optimization (PSO). The framework uses a master swarm for evolving complete solutions of the problem, and uses a set of slave swarms for evolving sub-solutions of the subproblems concurrently. The master swarm and slave swarms alternatively implement the PSO procedure to improve the problem-solving efficiency. Using the asynchronous team based agent architecture, a master/slave swarm consists of different kinds of agents, which share a population of solutions and cooperate to evolve the population, such as initializing solutions, moving particles, handling constraints, and decomposing/synthesizing sub-solutions. The framework can be used to solve complicated constained and multiobjective optimization problems efficiently. Experimental results demonstrate that this approach has significant performance advantage over two other state-of-the-art algorithms on a typical transportation problem.
Keywords:agent  particle swarm optimization (PSO)  master-slave model  cooperative evolution  distributed computing
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